description

Smart Spaces enhance user capabilities and comfort by providing new services and automated service execution based on sensed user activity. Though, when becoming really "smart", such spaces should not only provide some service automation, but further learn and adapt their behavior during use. This article motivates and investigates learning of situation models in a smart space, covering knowledge acquisition from observation as well as evolving situation models during use. An integral framework for acquiring and evolving different layers of a situation model is detailed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and the evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. An implementation of the whole framework for a smart home environment is described, and the results of several evaluations are depicted.